Prediction of places of visit using tweets

Chauhan, Arun; Kummamuru, Krishna; Toshniwal, Durga

doi:10.1007/s10115-016-0936-x

Cited by 20 publications

(3 citation statements)

References 30 publications

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“…Opinions pools and politic elections predictions have been proposed to be correlated with the volume of tweets by using Sentiment Analysis techniques in O'Connor et al [43]. Different models based on volume of tweets and other means have been also used for predicting purposes: voting results in Bermingham and Smeaton [3] and in Tumasjan et al [56], economics [4,15], marketability of consumer goods [50], public health seasonal flu [1,34,51], box-office revenues for movies [2,36,38,54], crimes [58], book sales [26], recommendations on places to be visited [14] and weather forecast information [24,25]. Moreover, Twitter-based metrics have been used to predict and estimate the number of people in some location, such as airports, the so-called crowd size estimation by the work of Botta et al [5], as well as to predict the audience of scheduled television programmes, where the audience is highly involved, such as it occurs with reality shows (i.e., X Factor and Pechino Express, in Italy) [17].…”

Section: Related Workmentioning

confidence: 99%

Assessing the reTweet proneness of tweets: predictive models for retweeting

Nesi

Pantaleo

Paoli

et al. 2018

Multimed Tools Appl

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The problem of assessing the mechanisms underlying the phenomenon of virality of social network posts is of great value for many activities, such as advertising and viral marketing, influencing and promoting, early monitoring and emergency response. Among the several social networks, Twitter.com is one of the most effective in propagating information in real time, and the propagation effectiveness of a post (i.e., tweet) is related to the number of times the tweet has been retweeted. Different models have been proposed in the literature to understand the retweet proneness of a tweet (tendency or inclination of a tweet to be retweeted). In this paper, a further step is presented, thus several features extracted from Twitter data have been analyzed to create predictive models, with the aim of predicting the degree of retweeting of tweets (i.e., the number of retweets a given tweet may get). The main goal is to obtain indications about the probable number of retweets a tweet may obtain from the social network. In the paper, the usage of the classification trees with recursive partitioning procedure for prediction has been proposed and the obtained results have been compared, in terms of accuracy and processing time, with respect to other methods. The Twitter data employed for the proposed study have been collected by using the Twitter Vigilance study and research platform of DISIT Lab in the last 18 months. The work has been developed in the context of smart city projects of the European Commission RESOLUTE H2020, in which the

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Section: Related Workmentioning

confidence: 99%

Assessing the reTweet proneness of tweets: predictive models for retweeting

Nesi

Pantaleo

Paoli

et al. 2018

Multimed Tools Appl

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“…In the context of the Participatory Web or simply Web 2.0, social media provide an effective, sophisticated and powerful way to gather preferences and activities of groups of the population. For example, data that are produced on the social media services can generate a complex and adequate knowledge on a plethora of fields of application, such as economy (stock market analysis [26] and private consumption prediction [27]), politics (opinion polls [28] and predictions of political elections [29]), sports (predict football game results [30]), tourism (places to be visited by observing the most frequently attended places in a given location [31]), demographics (identifying gender and age of selected user groups [32]) and infotainment [33]. Approaches for exploiting social media data are already mature enough, going beyond research prototypes, to mature robust data analytics software products such as Sysomos, Keyhole, Agorapulse, and the Twitris platform.…”

Section: Related Workmentioning

confidence: 99%

Semantic Interoperability for IoT Platforms in Support of Decision Making: An Experiment on Early Wildfire Detection

Kalatzis

Routis

Marinellis

et al. 2019

Sensors

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One of the main obstacles towards the promotion of IoT adoption and innovation is data interoperability. Facilitating cross-domain interoperability is expected to be the core element for the realisation of the next generation of the IoT computing paradigm that is already taking shape under the name of Internet of Everything (IoE). In this article, an analysis of the current status on IoT semantic interoperability is presented that leads to the identification of a set of generic requirements that act as fundamental design principles for the specification of interoperability enabling solutions. In addition, an extension of NGSIv2 data model and API (de-facto) standards is proposed aiming to bridge the gap among IoT and social media and hence to integrate user communities with cyber-physical systems. These specifications have been utilised for the implementation of the IoT2Edge interoperability enabling mechanism which is evaluated within the context of a catastrophic wildfire incident that took place in Greece on July 2018. Weather data, social media activity, video recordings from the fire, sensor measurements and satellite data, linked to the location and the time of this fire incident have been collected, modeled in a uniform manner and fed to an early fire detection decision support system. The findings of the experiment certify that achieving minimum data interoperability with light-weight, plug-n-play mechanisms can be realised with significant benefits for our society.

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“…[56], places to be visited observing the most frequently attended places in a given location [8]. In addition, Twitter data has been used for assessing weather forecast information in [17], and in [18].…”

Section: Related Workmentioning

confidence: 99%

Predicting TV programme audience by using twitter based metrics

Crisci

Grasso

Nesi

et al. 2017

Multimed Tools Appl

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The predictive capabilities of metrics based on Twitter data have been stressed in different fields: business, health, market, politics, etc. In specific cases, a deeper analysis is required to create useful metrics and models with predicting capabilities. In this paper, a set of metrics based on Twitter data have been identified and presented in order to predict the audience of scheduled television programmes, where the audience is highly involved such as it occurs with reality shows (i.e., X Factor and Pechino Express, in Italy). Identified suitable metrics are based on the volume of tweets, the distribution of linguistic elements, the volume of distinct users involved in tweeting, and the sentiment analysis of tweets. On this ground a number of predictive models have been identified and compared. The resulting method has been selected in the context of a validation and assessment by using real data, with the aim of building a flexible framework able to exploit the predicting capabilities of social media data. Further details are reported about the method adopted to build models which focus on the identification of predictors by their statistical significance. Experiments have been based on the collected Twitter data by using Twitter Vigilance platform, which is presented in this paper, as well.Multimed Tools Appl

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Prediction of places of visit using tweets

Cited by 20 publications

References 30 publications

Assessing the reTweet proneness of tweets: predictive models for retweeting

Assessing the reTweet proneness of tweets: predictive models for retweeting

Semantic Interoperability for IoT Platforms in Support of Decision Making: An Experiment on Early Wildfire Detection

Predicting TV programme audience by using twitter based metrics

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